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library(velocyto.R)

载入需要的程辑包：Matrix

library(SeuratWrappers)

Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

library(Seurat)

载入程辑包：‘Seurat’

The following objects are masked from ‘package:SeuratWrappers’:

    ALRAChooseKPlot, ExportToCellbrowser, ReadAlevin, RunALRA, StopCellbrowser

source("tianfengRwrappers.R")

载入需要的程辑包：dplyr

载入程辑包：‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

载入需要的程辑包：reticulate
载入需要的程辑包：tidyr

载入程辑包：‘tidyr’

The following objects are masked from ‘package:Matrix’:

    expand, pack, unpack


载入程辑包：‘MySeuratWrappers’

The following objects are masked from ‘package:Seurat’:

    DimPlot, DoHeatmap, LabelClusters, RidgePlot, VlnPlot


载入程辑包：‘cowplot’

The following object is masked from ‘package:ggpubr’:

    get_legend

载入需要的程辑包：viridisLite

载入程辑包：‘reshape2’

The following object is masked from ‘package:tidyr’:

    smiths

NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
      Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
      if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow

Registered S3 method overwritten by 'enrichplot':
  method               from
  fortify.enrichResult DOSE
clusterProfiler v3.14.3  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
Registering fonts with R

载入程辑包：‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

载入需要的程辑包：Biobase
载入需要的程辑包：BiocGenerics
载入需要的程辑包：parallel

载入程辑包：‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply,
    parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:dplyr’:

    combine, intersect, setdiff, union

The following object is masked from ‘package:Matrix’:

    which

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated,
    eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match,
    mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames,
    sapply, setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

载入需要的程辑包：e1071

载入程辑包：‘widgetTools’

The following object is masked from ‘package:dplyr’:

    funs


载入程辑包：‘DynDoc’

The following object is masked from ‘package:BiocGenerics’:

    path


载入程辑包：‘DT’

The following object is masked from ‘package:Seurat’:

    JS

载入需要的程辑包：S4Vectors
载入需要的程辑包：stats4

载入程辑包：‘S4Vectors’

The following object is masked from ‘package:plotly’:

    rename

The following object is masked from ‘package:tidyr’:

    expand

The following objects are masked from ‘package:dplyr’:

    first, rename

The following object is masked from ‘package:Matrix’:

    expand

The following object is masked from ‘package:base’:

    expand.grid

载入需要的程辑包：IRanges

载入程辑包：‘IRanges’

The following object is masked from ‘package:plotly’:

    slice

The following objects are masked from ‘package:dplyr’:

    collapse, desc, slice

载入需要的程辑包：GenomicRanges
载入需要的程辑包：GenomeInfoDb
载入需要的程辑包：SummarizedExperiment
载入需要的程辑包：DelayedArray
载入需要的程辑包：matrixStats

载入程辑包：‘matrixStats’

The following objects are masked from ‘package:Biobase’:

    anyMissing, rowMedians

The following object is masked from ‘package:dplyr’:

    count

载入需要的程辑包：BiocParallel

载入程辑包：‘DelayedArray’

The following objects are masked from ‘package:matrixStats’:

    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges

The following object is masked from ‘package:clusterProfiler’:

    simplify

The following objects are masked from ‘package:base’:

    aperm, apply, rowsum


载入程辑包：‘SummarizedExperiment’

The following object is masked from ‘package:Seurat’:

    Assays

#首先需要取并集，然后使用已有的umap，在其上绘制速率图
velodata1 <- ReadVelocity(file ="./velo_data/CARconHEL.loom")

reading loom file via hdf5r...

velodata2 <- ReadVelocity(file ="./velo_data/CARconHEA0620.loom")

reading loom file via hdf5r...

velodata3 <- ReadVelocity(file ="./velo_data/CARconHEA0717.loom")

reading loom file via hdf5r...

#PA
# velodata1 <- ReadVelocity(file ="./velo_data/CARconDIS.loom")
# velodata2 <- ReadVelocity(file ="./velo_data/CARconDIS0620.loom")
# velodata3 <- ReadVelocity(file ="./velo_data/CARconDIS0717.loom")

#匹配两次的barcode
func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"1",sep = "-")
  return(s)
}

velodata1[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["spliced"]]@Dimnames[[2]],func))
velodata1[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata1[["unspliced"]]@Dimnames[[2]],func))
velodata1[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata1[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"3",sep = "-")
  return(s)
}

velodata2[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["spliced"]]@Dimnames[[2]],func))
velodata2[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata2[["unspliced"]]@Dimnames[[2]],func))
velodata2[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata2[["ambiguous"]]@Dimnames[[2]],func))


func <- function(s)
{
  s <- strsplit(s,".*:",fixed = F)[[1]][2]
  s <- strsplit(s,"x",fixed = T)[[1]]
  s <- paste(s,"5",sep = "-")
  return(s)
}

velodata3[["spliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["spliced"]]@Dimnames[[2]],func))
velodata3[["unspliced"]]@Dimnames[[2]] = as.character(lapply(velodata3[["unspliced"]]@Dimnames[[2]],func))
velodata3[["ambiguous"]]@Dimnames[[2]] = as.character(lapply(velodata3[["ambiguous"]]@Dimnames[[2]],func))

# CA_dataset2 <- readRDS("CA_dataset2.rds")
ds2_AC <- readRDS("ds2_AC.rds")
sp1_velo <- as.Seurat(x = velodata1)

  |                                                                                                               
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sp2_velo <- as.Seurat(x = velodata2)

  |                                                                                                               
  |                                                                                                         |   0%Warning: Non-unique features (rownames) present in the input matrix, making unique

  |                                                                                                               
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sp3_velo <- as.Seurat(x = velodata3)

  |                                                                                                               
  |                                                                                                         |   0%Warning: Non-unique features (rownames) present in the input matrix, making unique

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  |=========================================================================================================| 100%

merge_velo <- merge(sp1_velo, c(sp2_velo,sp3_velo))
merge_velo <- subset(merge_velo, cells = WhichCells(ds2_AC))
rm(sp1_velo)
rm(sp2_velo)
rm(sp3_velo)
rm(velodata1)
rm(velodata2)
rm(velodata3)

merge_velo <- readRDS("dataset2_PA_velo.RDS")

Loading required package: Seurat
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

# saveRDS(merge_velo,"dataset2_AC_velo.RDS")
# merge_velo <- readRDS("dataset2_AC_velo.RDS")
ident.colors <- colors_list[1:length(x = levels(x = merge_velo))]
names(x = ident.colors) <- levels(x = merge_velo)
cell.colors <- ident.colors[Idents(object = merge_velo)]

names(x = cell.colors) <- colnames(x = merge_velo)
png("velocity.png")
show.velocity.on.embedding.cor(emb = Embeddings(object = merge_velo, reduction = "umap"), vel = Tool(object = merge_velo, slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5), 
    cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1, 
    do.par = FALSE, cell.border.alpha = 0.1)

delta projections ... sqrt knn ... transition probs ... done
calculating arrows ... done
grid estimates ... grid.sd= 0.5051766  min.arrow.size= 0.01010353  max.grid.arrow.length= 0.04539128  done

dev.off()

null device 
          1 

# plot <- tSNE.velocity.plot(vel = Tool(object = sp1_velo, slot = "RunVelocity"))

source("../vascular-analysis/seuratToAnnDataCombined.R")

h5ad_output <- "AC_SMC_velo.h5ad"  

library(reticulate)
library(Matrix)
writeMM(t(merge_velo@assays$RNA@counts), file='combined.mtx')

NULL

writeMM(t(merge_velo@assays$spliced@counts), file='spliced.mtx')

NULL

writeMM(t(merge_velo@assays$unspliced@counts), file='unspliced.mtx')

NULL

write.csv(rownames(merge_velo@assays$spliced@counts), file = "genes.csv", row.names = FALSE)
write.csv(merge_velo@reductions$umap@cell.embeddings, file = "umap.csv", row.names = FALSE)
write.csv(merge_velo@reductions$pca@cell.embeddings, file = "pca.csv", row.names = FALSE)

write.csv(colnames(merge_velo@assays$spliced@counts), file = "cells.csv", row.names = FALSE)
write.csv(merge_velo@meta.data, file = "meta.csv", row.names = FALSE)
source_python('~/scRNAseq/vascular-analysis/build.py')
build(h5ad_output, pca = TRUE, umap = TRUE)
file.remove('combined.mtx')

[1] TRUE

file.remove('spliced.mtx')

[1] TRUE

file.remove('unspliced.mtx')

[1] TRUE

file.remove('genes.csv')

[1] TRUE

file.remove('cells.csv')

[1] TRUE

file.remove('umap.csv')

[1] TRUE

file.remove('pca.csv')

[1] TRUE

file.remove('meta.csv')

[1] TRUE

只在SMC亚群中进行

SMC_velo <- subset(merge_velo, idents = "SMC")
RunVelocity(object = SMC_velo, deltaT = 1, kCells = 25, fit.quantile = 0.02)
ident.colors <- colors_list[1:length(x = levels(x = SMC_velo))]
names(x = ident.colors) <- levels(x = SMC_velo)
cell.colors <- ident.colors[Idents(object = SMC_velo)]

names(x = cell.colors) <- colnames(x = SMC_velo)
png("SMC_velocity.png")
show.velocity.on.embedding.cor(emb = Embeddings(object = SMC_velo, reduction = "umap"), vel = Tool(object = SMC_velo, slot = "RunVelocity"), n = 200, scale = "sqrt", cell.colors = ac(x = cell.colors, alpha = 0.5), 
    cex = 0.8, arrow.scale = 3, show.grid.flow = TRUE, min.grid.cell.mass = 0.5, grid.n = 40, arrow.lwd = 1, 
    do.par = FALSE, cell.border.alpha = 0.1)
dev.off()

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